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Title: | QUANTUM ALGORITHMS FOR PROVABLE MACHINE LEARNING | Authors: | YANG SIYI | Keywords: | quantum speedup, amplitude amplification, p-concept learnable, inner product estimation, sampling | Issue Date: | 20-Aug-2021 | Citation: | YANG SIYI (2021-08-20). QUANTUM ALGORITHMS FOR PROVABLE MACHINE LEARNING. ScholarBank@NUS Repository. | Abstract: | The thesis explores the quantum speedups for various machine learning algorithms, including the neural network, the Hedge algorithm, the Ising model and Markov Random Fields, with provable learning guarantees. A main subroutine in these quantizations is the inner product estimation of vectors. The exact computation of inner product is first replaced with estimation. Then the estimation is sped-up using quantum amplitude amplification. Then a quadratic speedup in terms of the data dimension is obtained. | URI: | https://scholarbank.nus.edu.sg/handle/10635/212696 |
Appears in Collections: | Ph.D Theses (Open) |
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